System and method for managing respiratory insufficiency in conjunction with heart failure assessment

ABSTRACT

A system and method for evaluating a patient status from sampled physiometry for use in respiratory insufficiency management and heart failure assessment is presented. Physiological measures are stored and include direct measures regularly recorded on a substantially continuous basis by a medical device for a patient or measures derived from the direct measures. The physiological measures are sampled, which each relate to a same type of physiometry, and those of the physiological measures, which each relate to a different type of physiometry. A status is determined through analysis of those sampled physiological measures assembled from a plurality of recordation points. The sampled physiological measures are evaluated. Any trends are identified and include one of a status quo and a change, which might affect cardiac performance or respiratory performance. Each such trend is compared to applicable indications of worsening heart failure and respiratory insufficiency to generate a notification of parameter violations.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a continuation of U.S. patent applicationSer. No. 10/646,243, filed Aug. 22, 2003, pending; which is acontinuation of U.S. Pat. No. 7,207,945, issued Apr. 24, 2007; which isa continuation of U.S. Pat. No. 6,398,728, issued Jun. 4, 2002, thedisclosures of which are incorporated by reference.

FIELD

The present invention relates in general to heart failure assessment,and, in particular, to a system and method for managing respiratoryinsufficiency in conjunction with heart failure assessment.

BACKGROUND

Presently, respiratory insufficiency due to primary diseases of thelungs is one of the leading causes of acute and chronic illness in theworld. Clinically, respiratory insufficiency involves either difficultyin ventilation or in oxygenation. The former is manifest by increases inthe arterial partial pressure of carbon dioxide and the latter ismanifest by decreases in arterial partial pressure of oxygen. Forpurposes of this invention, the term “respiratory insufficiency” willrefer to ventilatory insufficiency and/or to problems in oxygenation dueto diseases of the lung. Common causes of respiratory insufficiencyinclude bronchitis, emphysema, pneumonia, pulmonary emboli, congestiveheart failure, tumor infiltration of the lung and abnormalities of theinterstitium of the lungs that may be infectious in origin, due toimmunological abnormalities, or as a result of exposure to environmentalpathogens. The effects of respiratory insufficiency range from cough toimpairment during physical exertion to a complete failure of lungfunction and respiratory arrest at any level of activity. Clinicalmanifestations of respiratory insufficiency include respiratorydistress, such as shortness of breath and fatigue, cough, and reducedexercise capacity or tolerance.

Several factors make the early diagnosis and prevention of respiratoryinsufficiency, as well as the monitoring of the progression ofrespiratory insufficiency, relatively difficult. First, the onset ofrespiratory insufficiency is generally subtle and erratic. Often, thesymptoms are ignored and the patient compensates by changing his or herdaily activities. This situation is especially true in chronic lungdisorders where the onset of symptoms can be very gradual. As a result,many respiratory insufficiency conditions or deteriorations inrespiratory insufficiency remain undiagnosed until more serious problemsarise seriously limiting the activities of daily living.

The susceptibility to suffer from respiratory insufficiency depends uponthe patient's age, sex, physical condition, and other factors, such assmoking history, occupation, diabetes, co-existing heart disease,immunodepression, the presence or absence of cancer, surgical history,kidney function, and extent of pre-existing lung disease. No one factoris dispositive. Finally, annual or even monthly lung checkups, includingchest X-rays or other lung tests, provide, at best, a “snapshot” ofpatient wellness and the incremental and subtle clinicophysiologicalchanges which portend the onset or progression of respiratoryinsufficiency often go unnoticed, even with regular health care.Documentation of subtle improvements following therapy that can guideand refine further evaluation and therapy can be equally elusive.

Nevertheless, taking advantage of frequently and regularly measuredphysiological measures, such as recorded manually by a patient, via anexternal monitoring or therapeutic device, or via implantable devicetechnologies, can provide a degree of detection and preventionheretofore unknown. For instance, patients already suffering from someform of treatable heart disease often receive an implantable pulsegenerator (IPG), cardiovascular or heart failure monitor, therapeuticdevice, or similar external wearable device, with which rhythm andstructural problems of the heart can be monitored and treated. Thesetypes of devices, although usually originally intended for use intreating some type of cardiac problem, can contain sufficientphysiological data to allow accurate assessment of lung disorders. Suchdevices are useful for detecting physiological changes in patientconditions through the retrieval and analysis of telemetered signalsstored in an on-board, volatile memory. Typically, these devices canstore more than thirty minutes of per heartbeat and respiratory cycledata recorded on a per heartbeat, per respiration, binned average basis,or on a derived basis from, for example, atrial or ventricularelectrical activity, minute ventilation, patient activity score, cardiacoutput score, arterial or mixed venous oxygen score, cardiopulmonarypressure measures, and the like. However, the proper analysis ofretrieved telemetered signals requires detailed medical subspecialtyknowledge, particularly by pulmonologists and cardiologists.

Alternatively, these telemetered signals can be remotely collected andanalyzed using an automated patient care system. One such system isdescribed in a related, commonly assigned U.S. Pat. No. 6,312,378,issued Nov. 6, 2001, the disclosure of which is incorporated herein byreference. A medical device adapted to be implanted in an individualpatient records telemetered signals that are then retrieved on aregular, periodic basis using an interrogator or similar interfacingdevice. The telemetered signals are downloaded via an internetwork ontoa network server on a regular, e.g., daily, basis and stored as sets ofcollected measures in a database along with other patient care records.The information is then analyzed in an automated fashion and feedback,which includes a patient status indicator, is provided to the patient.

While such an automated system can serve as a valuable tool in providingremote patient care, an approach to systematically correlating andanalyzing the raw collected telemetered signals, as well as manuallycollected physiological measures, through applied pulmonary andcardiovascular medical knowledge to accurately diagnose the onset of aparticular medical condition, such as respiratory insufficiency, isneeded, especially in patients with co-existing heart disease. Oneautomated patient care system directed to a patient-specific monitoringfunction is described in U.S. Pat. No. 5,113,869 ('869) to Nappholz etal. The '869 patent discloses an implantable, programmableelectrocardiography (ECG) patient monitoring device that senses andanalyzes ECG signals to detect ECG and physiological signalcharacteristics predictive of malignant cardiac arrhythmias. Themonitoring device can communicate a warning signal to an external devicewhen arrhythmias are predicted. However, the Nappholz device is limitedto detecting tachycardias. Unlike requirements for automated respiratoryinsufficiency monitoring, the Nappholz device focuses on rudimentary ECGsignals indicative of malignant cardiac tachycardias, an already wellestablished technique that can be readily used with on-board signaldetection techniques. Also, the Nappholz device is patient specific onlyand is unable to automatically take into consideration a broader patientor peer group history for reference to detect and consider theprogression or improvement of lung disease. Moreover, the Nappholzdevice has a limited capability to automatically self-reference multipledata points in time and cannot detect disease regression even in theindividual patient. Also, the Nappholz device must be implanted andcannot function as an external monitor. Finally, the Nappholz device isincapable of tracking the cardiovascular and cardiopulmonaryconsequences of any rhythm disorder.

Consequently, there is a need for a systematic approach to detectingtrends in regularly collected physiological data indicative of theonset, progression, regression, or status quo of respiratoryinsufficiency diagnosed and monitored using an automated, remote patientcare system. The physiological data could be telemetered signals datarecorded either by an external or an implantable medical device or,alternatively, individual measures collected through manual means.Preferably, such an approach would be capable of diagnosing both acuteand chronic respiratory insufficiency conditions, as well as thesymptoms of other lung disorders. In addition, findings from individual,peer group, and general population patient care records could beintegrated into continuous, on-going monitoring and analysis.

SUMMARY

The present invention provides a system and method for diagnosing andmonitoring the onset, progression, regression, and status quo ofrespiratory insufficiency using an automated collection and analysispatient care system. Measures of patient cardiopulmonary information areeither recorded by an external or implantable medical device, such as anIPG, cardiovascular or heart failure monitor, or respiratory diagnosticor therapeutic device, or manually through conventional patient-operablemeans. The measures are collected on a regular, periodic basis forstorage in a database along with other patient care records. Derivedmeasures are developed from the stored measures. Select stored andderived measures are analyzed and changes in patient condition arelogged. The logged changes are compared to quantified indicatorthresholds to detect findings of respiratory distress or reducedexercise capacity indicative of the principal pathophysiologicalmanifestations of respiratory insufficiency: elevated partial pressureof arterial carbon dioxide and reduced partial pressure of arterialoxygen.

An embodiment provides a system and method for managing respiratoryinsufficiency in conjunction with heart failure assessment.Physiological measures are assembled. The physiological measures weredirectly recorded as data on a substantially continuous basis by amedical device for a patient or indirectly derived from the data. Astatus for the patient is determined through sampling and analysis ofthe physiological measures over a plurality of data assembly points. Thephysiological measures are evaluated relative to the patient status byanalyzing any trend including one of a status quo and a change in atleast one of cardiac performance and respiratory performance andcomparing the trend to applicable indications of worsening heart failureand respiratory insufficiency.

A further embodiment provides a system and method for evaluating apatient status from sampled physiometry for use in respiratoryinsufficiency management and heart failure assessment. Physiologicalmeasures are stored and include at least one of direct measuresregularly recorded on a substantially continuous basis by a medicaldevice for a patient and measures derived from the direct measures. Atleast one of those of the physiological measures are sampled, which eachrelate to a same type of physiometry, and those of the physiologicalmeasures, which each relate to a different type of physiometry. A statusfor the patient is determined through analysis of those sampledphysiological measures assembled from a plurality of recordation points.The sampled physiological measures are evaluated. Any trends that areindicated by the patient status are identified and include one of astatus quo and a change, which might affect one or more of cardiacperformance and respiratory performance of the patient. Each such trendis compared to applicable indications of worsening heart failure andrespiratory insufficiency to generate a notification of parameterviolations.

The present invention provides a capability to detect and track subtletrends and incremental changes in recorded patient cardiopulmonaryinformation for diagnosing and monitoring respiratory insufficiency.When coupled with an enrollment in a remote patient monitoring servicehaving the capability to remotely and continuously collect and analyzeexternal or implantable medical device measures, respiratoryinsufficiency detection, prevention and tracking regression fromtherapeutic maneuvers become feasible.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an automated collection and analysispatient care system for diagnosing and monitoring respiratoryinsufficiency and outcomes thereof in accordance with the presentinvention;

FIG. 2 is a database schema showing, by way of example, the organizationof a device and derived measures set record for care of patients withrespiratory insufficiency stored as part of a patient care record in thedatabase of the system of FIG. 1;

FIG. 3 is a database schema showing, by way of example, the organizationof a quality of life and symptom measures set record for care ofpatients with respiratory insufficiency stored as part of a patient carerecord in the database of the system of FIG. 1;

FIG. 4 is a database schema showing, by way of example, the organizationof a combined measures set record for care of patients with respiratoryinsufficiency stored as part of a patient care record in the database ofthe system of FIG. 1;

FIG. 5 is a block diagram showing the software modules of the serversystem of the system of FIG. 1;

FIG. 6 is a record view showing, by way of example, a set of partialpatient care records for care of patients with respiratory insufficiencystored in the database of the system of FIG. 1;

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records of FIG. 6;

FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring respiratory insufficiency and outcomes thereof using anautomated collection and analysis patient care system in accordance withthe present invention;

FIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets for use in the method of FIGS. 8A-8B;

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets for use in the method of FIGS. 8A-8B;

FIGS. 11A-11F are flow diagrams showing the routine for testingthreshold limits for use in the method of FIGS. 8A-8B;

FIG. 12 is a flow diagram showing the routine for evaluating the onset,progression, regression, and status quo of respiratory insufficiency foruse in the method of FIGS. 8A-8B;

FIGS. 13A-13C are flow diagrams showing the routine for determining anonset of respiratory insufficiency for use in the routine of FIG. 12;

FIGS. 14A-14C are flow diagrams showing the routine for determiningprogression or worsening of respiratory insufficiency for use in theroutine of FIG. 12;

FIGS. 15A-15C are flow diagrams showing the routine for determiningregression or improving of respiratory insufficiency for use in theroutine of FIG. 12; and

FIG. 16 is a flow diagram showing the routine for determining thresholdstickiness (“hysteresis”) for use in the method of FIG. 12.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing an automated collection and analysispatient care system 10 for diagnosing and monitoring respiratoryinsufficiency in accordance with the present invention. An exemplaryautomated collection and analysis patient care system suitable for usewith the present invention is disclosed in the related, commonlyassigned U.S. Pat. No. 6,312,378, issued Nov. 6, 2001, the disclosure ofwhich is incorporated herein by reference. Preferably, an individualpatient 11 is a recipient of an implantable medical device 12, such as,by way of example, an IPG, cardiovascular, heart failure monitor,pulmonary monitor, or therapeutic device, with a set of leads extendinginto his or her heart and electrodes implanted throughout thecardiopulmonary system. Alternatively, an external monitoring ortherapeutic medical device 26, a subcutaneous monitor or device insertedinto other organs, a cutaneous monitor, or even a manual physiologicalmeasurement device, such as an respiratory monitor, electrocardiogram orheart rate monitor, could be used. The implantable medical device 12 andexternal medical device 26 include circuitry for recording into ashort-term, volatile memory telemetered signals stored for laterretrieval, which become part of a set of device and derived measures,such as described below, by way of example, with reference to FIG. 2.Exemplary implantable medical devices suitable for use in the presentinvention include the Discovery line of pacemakers, manufactured byGuidant Corporation, Indianapolis, Ind., and the Gem line of ICDs,manufactured by Medtronic Corporation, Minneapolis, Minn.

The telemetered signals stored in the implantable medical device 12 arepreferably retrieved upon the completion of an initial observationperiod and subsequently thereafter on a continuous, periodic (daily)basis, such as described in the related, commonly assigned U.S. Pat. No.6,221,011, issued Apr. 24, 2001, the disclosure of which is incorporatedherein by reference. A programmer 14, personal computer 18, or similardevice for communicating with an implantable medical device 12 can beused to retrieve the telemetered signals. A magnetized reed switch (notshown) within the implantable medical device 12 closes in response tothe placement of a wand 13 over the site of the implantable medicaldevice 12. The programmer 14 sends programming or interrogatinginstructions to and retrieves stored telemetered signals from theimplantable medical device 12 via RF signals exchanged through the wand13. Similar communication means are used for accessing the externalmedical device 26. Once downloaded, the telemetered signals are sent viaan internetwork 15, such as the Internet, to a server system 16 whichperiodically receives and stores the telemetered signals as devicemeasures in patient care records 23 in a database 17, as furtherdescribed below, by way of example, with reference to FIGS. 2 and 3. Anexemplary programmer 14 suitable for use in the present invention is theModel 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind.

The patient 11 is remotely monitored by the server system 16 via theinternetwork 15 through the periodic receipt of the retrieved devicemeasures from the implantable medical device 12 or external medicaldevice 26. The patient care records 23 in the database 17 are organizedinto two identified sets of device measures: an optional referencebaseline 26 recorded during an initial observation period and monitoringsets 27 recorded subsequently thereafter. The device measures sets areperiodically analyzed and compared by the server system 16 to indicatorthresholds corresponding to quantifiable physiological measures of apathophysiology indicative of respiratory insufficiency, as furtherdescribed below with reference to FIG. 5. As necessary, feedback isprovided to the patient 11. By way of example, the feedback includes anelectronic mail message automatically sent by the server system 16 overthe internetwork 15 to a personal computer 18 (PC) situated for localaccess by the patient 11. Alternatively, the feedback can be sentthrough a telephone interface device 19 as an automated voice mailmessage to a telephone 21 or as an automated facsimile message to afacsimile machine 22, both also situated for local access by the patient11. Moreover, simultaneous notifications can also be delivered to thepatient's physician, hospital, or emergency medical services provider 29using similar feedback means to deliver the information.

The server system 10 can consist of either a single computer system or acooperatively networked or clustered set of computer systems. Eachcomputer system is a general purpose, programmed digital computingdevice consisting of a central processing unit (CPU), random accessmemory (RAM), non-volatile secondary storage, such as a hard drive or CDROM drive, network interfaces, and peripheral devices, including userinterfacing means, such as a keyboard and display. Program code,including software programs, and data are loaded into the RAM forexecution and processing by the CPU and results are generated fordisplay, output, transmittal, or storage, as is known in the art.

The database 17 stores patient care records 23 for each individualpatient to whom remote patient care is being provided. Each patient carerecord 23 contains normal patient identification and treatment profileinformation, as well as medical history, medications taken, height andweight, and other pertinent data (not shown). The patient care records23 consist primarily of two sets of data: device and derived measures(D&DM) sets 24 a, 24 b and quality of life (QOL) sets 25 a, 25 b, theorganization of which are further described below with respect to FIGS.2 and 3, respectively. The device and derived measures sets 24 a, 24 band quality of life and symptom measures sets 25 a, 25 b can be furtherlogically categorized into two potentially overlapping sets. Thereference baseline 26 is a special set of device and derived referencemeasures sets 24 a and quality of life and symptom measures sets 25 arecorded and determined during an initial observation period. Monitoringsets 27 are device and derived measures sets 24 b and quality of lifeand symptom measures sets 25 b recorded and determined thereafter on aregular, continuous basis. Other forms of database organization arefeasible.

The implantable medical device 12 and, in a more limited fashion, theexternal medical device 26, record patient information for care ofpatients with respiratory insufficiency on a regular basis. The recordedpatient information is downloaded and stored in the database 17 as partof a patient care record 23. Further patient information can be derivedfrom recorded data, as is known in the art. FIG. 2 is a database schemashowing, by way of example, the organization of a device and derivedmeasures set record 40 for patient care stored as part of a patient carerecord in the database 17 of the system of FIG. 1. Each record 40 storespatient information which includes a snapshot of telemetered signalsdata which were recorded by the implantable medical device 12 or theexternal medical device 26, for instance, on per heartbeat, binnedaverage or derived bases; measures derived from the recorded devicemeasures; and manually collected information, such as obtained through apatient medical history interview or questionnaire. The followingnon-exclusive information can be recorded for a patient: atrialelectrical activity 41, ventricular electrical activity 42, PR intervalor AV interval 43, QRS measures 44, ST-T wave measures 45, QT interval46, body temperature 47, patient activity score 48, posture 49,cardiovascular pressures 50, pulmonary artery systolic pressure measure51, pulmonary artery diastolic pressure measure 52, respiratory rate 53,ventilatory tidal volume 54, minute ventilation 55, transthoracicimpedance 56, cardiac output 57, systemic blood pressure 58, patientgeographic location (altitude) 59, mixed venous oxygen score 60,arterial oxygen score 61, arterial carbon dioxide score 62, acidity (pH)level 63, potassium [K+] level 64, sodium [Na+] level 65, glucose level66, blood urea nitrogen (BUN) and creatinine 67, hematocrit 68, hormonallevels 69, lung injury chemical tests 70, cardiac injury chemical tests71, myocardial blood flow 72, central nervous system (CNS) injurychemical tests 73, central nervous system blood flow 74, interventionsmade by the implantable medical device or external medical device 75,and the relative success of any interventions made 76. In addition, theimplantable medical device or external medical device communicatesdevice-specific information, including battery status, general devicestatus and program settings 77 and the time of day 78 for the variousrecorded measures. Other types of collected, recorded, combined, orderived measures are possible, as is known in the art.

The device and derived measures sets 24 a, 24 b (shown in FIG. 1), alongwith quality of life and symptom measures sets 25 a, 25 b, as furtherdescribed below with reference to FIG. 3, are continuously andperiodically received by the server system 16 as part of the on-goingpatient care monitoring and analysis function. These regularly collecteddata sets are collectively categorized as the monitoring sets 27 (shownin FIG. 1). In addition, select device and derived measures sets 24 aand quality of life and symptom measures sets 25 a can be designated asa reference baseline 26 at the outset of patient care to improve theaccuracy and meaningfulness of the serial monitoring sets 27. Selectpatient information is collected, recorded, and derived during aninitial period of observation or patient care, such as described in therelated, commonly assigned U.S. Pat. No. 6,221,011, issued Apr. 24,2001, the disclosure of which is incorporated herein by reference.

As an adjunct to remote patient care through the monitoring of measuredphysiological data via the implantable medical device 12 or externalmedical device 26, quality of life and symptom measures sets 25 a canalso be stored in the database 17 as part of the reference baseline 26,if used, and the monitoring sets 27. A quality of life measure is asemi-quantitative self-assessment of an individual patient's physicaland emotional well being and a record of symptoms, such as provided bythe Duke Activities Status Indicator. These scoring systems can beprovided for use by the patient 11 on the personal computer 18 (shown inFIG. 1) to record his or her quality of life scores for both initial andperiodic download to the server system 16. FIG. 3 is a database schemashowing, by way of example, the organization of a quality of life record80 for use in the database 17. The following information is recorded fora patient: overall health wellness 81, psychological state 82,activities of daily living 83, work status 84, geographic location 85,family status 86, shortness of breath 87, cough 88, sputum production89, sputum color 90, energy level 91, exercise tolerance 92, chestdiscomfort 93, and time of day 94, and other quality of life and symptommeasures as would be known to one skilled in the art.

The patient may also add non-device quantitative measures, such as thesix-minute walk distance, as complementary data to the device andderived measures sets 24 a, 24 b and the symptoms during the six-minutewalk to quality of life and symptom measures sets 25 a, 25 b.

Other types of quality of life and symptom measures are possible, suchas those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W.B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, adapted for usefor lung disease rather than heart disease, also described in Ibid.

On a periodic basis, the patient information stored in the database 17is analyzed and compared to pre-determined cutoff levels, which, whenexceeded, can provide etiological indications of respiratoryinsufficiency symptoms. FIG. 4 is a database schema showing, by way ofexample, the organization of a combined measures set record 95 for usein the database 17. Each record 95 stores patient information obtainedor derived from the device and derived measures sets 24 a, 24 b andquality of life and symptom measures sets 25 a, 25 b as maintained inthe reference baseline 26, if used, and the monitoring sets 27. Thecombined measures set 95 represents those measures most (but notexhaustively or exclusively) relevant to a pathophysiology indicative ofrespiratory insufficiency and are determined as further described belowwith reference to FIGS. 8A-8B. The following information is stored for apatient: heart rate 96, heart rhythm (e.g., normal sinus vs. atrialfibrillation) 97, pacing modality 98, pulmonary artery systolic pressuremeasure 99, pulmonary artery diastolic pressure measure 100, cardiacoutput score 101, arterial oxygen score 102, mixed venous oxygen score103, respiratory rate 104, tidal volume 105, transthoracic impedance106, arterial carbon dioxide score 107, right ventricular peak systolicpressure 108, pulmonary artery end diastolic pressure 109, patientactivity score 110, posture 111, exercise tolerance quality of life andsymptom measures 112, respiratory distress quality of life and symptommeasures 113, cough 114, sputum production 115, any interventions madeto treat respiratory insufficiency 116, including treatment by medicaldevice, via drug infusion administered by the patient or by a medicaldevice, surgery, and any other form of medical intervention as is knownin the art, the relative success of any such interventions made 117, andtime of day 118. Other types of comparison measures regardingrespiratory insufficiency are possible as is known in the art. In thedescribed embodiment, each combined measures set 95 is sequentiallyretrieved from the database 17 and processed. Alternatively, eachcombined measures set 95 could be stored within a dynamic data structuremaintained transitorily in the random access memory of the server system16 during the analysis and comparison operations.

FIG. 5 is a block diagram showing the software modules of the serversystem 16 of the system 10 of FIG. 1. Each module is a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU of the server system 16 as object or byte code, as is known inthe art. The various implementations of the source code and object andbyte codes can be held on a computer-readable storage medium or embodiedon a transmission medium in a carrier wave. The server system 16includes three primary software modules, database module 125, diagnosticmodule 126, and feedback module 128, which perform integrated functionsas follows.

First, the database module 125 organizes the individual patient carerecords 23 stored in the database 17 (shown in FIG. 1) and efficientlystores and accesses the reference baseline 26, monitoring sets 27, andpatient care data maintained in those records. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

Next, the diagnostic module 126 makes findings of respiratoryinsufficiency based on the comparison and analysis of the data measuresfrom the reference baseline 26 and monitoring sets 27. The diagnosticmodule includes three modules: comparison module 130, analysis module131, and quality of life module 132. The comparison module 130 comparesrecorded and derived measures retrieved from the reference baseline 26,if used, and monitoring sets 27 to indicator thresholds 129. Thedatabase 17 stores individual patient care records 23 for patientssuffering from various health disorders and diseases for which they arereceiving remote patient care. For purposes of comparison and analysisby the comparison module 130, these records can be categorized into peergroups containing the records for those patients suffering from similardisorders, as well as being viewed in reference to the overall patientpopulation. The definition of the peer group can be progressivelyrefined as the overall patient population grows. To illustrate, FIG. 6is a record view showing, by way of example, a set of partial patientcare records for care of patients with respiratory insufficiency storedin the database 17 for three patients, Patient 1, Patient 2, and Patient3. For each patient, three sets of peer measures, X, Y, and Z, areshown. Each of the measures, X, Y, and Z, could be either collected orderived measures from the reference baseline 26, if used, and monitoringsets 27.

The same measures are organized into time-based sets with Set 0representing sibling measures made at a reference time t=0. Similarly,Set n−2, Set n−1 and Set n each represent sibling measures made at laterreference times t=n−2, t=n−1 and t=n, respectively. Thus, for a givenpatient, such as Patient 1, serial peer measures, such as peer measureX₀ through X_(n), represent the same type of patient informationmonitored over time. The combined peer measures for all patients can becategorized into a health disorder- or disease-matched peer group. Thedefinition of disease-matched peer group is a progressive definition,refined over time as the number of monitored patients grows. Measuresrepresenting different types of patient information, such as measuresX₀, Y₀, and Z₀, are sibling measures. These are measures which are alsomeasured over time, but which might have medically significant meaningwhen compared to each other within a set for an individual patient.

The comparison module 130 performs two basic forms of comparisons.First, individual measures for a given patient can be compared to otherindividual measures for that same patient (self-referencing). Thesecomparisons might be peer-to-peer measures, that is, measures relatingto a one specific type of patient information, projected over time, forinstance, X_(n), X_(n-1), X_(n-2), . . . X₀, or sibling-to-siblingmeasures, that is, measures relating to multiple types of patientinformation measured during the same time period, for a single snapshot,for instance, X_(n), Y_(n), and Z_(n), or projected over time, forinstance, X_(n), Y_(n), Z_(n), X_(n-1), Y_(n-1), Z_(n-1), X_(n-2),Y_(n-2), Z_(n-2), . . . X₀, Y₀, Z₀. Second, individual measures for agiven patient can be compared to other individual measures for a groupof other patients sharing the same disorder- or disease-specificcharacteristics (peer group referencing) or to the patient population ingeneral (population referencing). Again, these comparisons might bepeer-to-peer measures projected over time, for instance, X_(n), X_(n′),X_(n″), X_(n-1), X_(n-1′), X_(n-1″), X_(n-2), X_(n-2′), X_(n-2″) . . .X₀, X_(0′), X_(0″), or comparing the individual patient's measures to anaverage from the group. Similarly, these comparisons might besibling-to-sibling measures for single snapshots, for instance, X_(n),X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), and Z_(n), Z_(n′), Z_(n″), orprojected over time, for instance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′),Y_(n″), Z_(n), Z_(n′), Z_(n″), X_(n-1), X_(n-1′), X_(n-1″), Y_(n-1),Y_(n-1′), Y_(n-1″), Z_(n-1), Z_(n-1′), Z_(n-1″), X_(n-2), X_(n-2′),X_(n-2′), Y_(n-2), Y_(n-2′), Y_(n-2″), Z_(n-2), Z_(n-2′), Z_(n-2″) . . .X₀, X_(0′), X_(0″), Y₀, Y_(0′), Y_(0″), and Z₀, Z_(0′), Z_(0″). Otherforms of comparisons are feasible, including multiple disease diagnosesfor diseases exhibiting similar abnormalities in physiological measuresthat result from a second disease but manifest in different combinationsor onset in different temporal sequences.

FIG. 7 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records 23 of FIG. 1. Each patient carerecord 23 includes characteristics data 350, 351, 352, includingpersonal traits, demographics, medical history, and related personaldata, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of chronic bronchitis, recurrent pneumonia, a history of aninferior myocardial infarction and diabetes. Similarly, thecharacteristics data 351 for patient 2 might include identical personaltraits, thereby resulting in partial overlap 353 of characteristics data350 and 351. Similar characteristics overlap 354, 355, 356 can existbetween each respective patient. The overall patient population 357would include the universe of all characteristics data. As themonitoring population grows, the number of patients with personal traitsmatching those of the monitored patient will grow, increasing the valueof peer group referencing. Large peer groups, well matched across allmonitored measures, will result in a well known natural history ofdisease and will allow for more accurate prediction of the clinicalcourse of the patient being monitored. If the population of patients isrelatively small, only some traits 356 will be uniformly present in anyparticular peer group. Eventually, peer groups, for instance, composedof 100 or more patients each, would evolve under conditions in whichthere would be complete overlap of substantially all salient data,thereby forming a powerful core reference group for any new patientbeing monitored.

Referring back to FIG. 5, the analysis module 131 analyzes the resultsfrom the comparison module 130, which are stored as a combined measuresset 95 (not shown), to a set of indicator thresholds 129, as furtherdescribed below with reference to FIGS. 8A-8B. Similarly, the quality oflife module 132 compares quality of life and symptom measures 25 a, 25 bfrom the reference baseline 26 and monitoring sets 27, the results ofwhich are incorporated into the comparisons performed by the analysismodule 131, in part, to either refute or support the findings based onphysiological “hard” data. Finally, the feedback module 128 providesautomated feedback to the individual patient based, in part, on thepatient status indicator 127 generated by the diagnostic module 126. Asdescribed above, the feedback could be by electronic mail or byautomated voice mail or facsimile. The feedback can also includenormalized voice feedback, such as described in the related, commonlyassigned U.S. Pat. No. 6,203,495, issued Mar. 20, 2001, the disclosureof which is incorporated herein by reference. In addition, the feedbackmodule 128 determines whether any changes to interventive measures areappropriate based on threshold stickiness (“hysteresis”) 133, as furtherdescribed below with reference to FIG. 16. The threshold stickiness 133can prevent fickleness in the diagnostic routines resulting fromtransient, non-trending and non-significant fluctuations in the variouscollected and derived measures in favor of more certainty in diagnosis.However, in the case of some of the parameters being followed, such asactivity and pulmonary artery systolic and diastolic pressures, abruptspikes in these measures can be indicative of coughing and thereforehelpful in indicating the onset of pulmonary insufficiency. In a furtherembodiment of the present invention, the feedback module 128 includes apatient query engine 134 that enables the individual patient 11 tointeractively query the server system 16 regarding the diagnosis,therapeutic maneuvers, and treatment regimen. Conversely, the patientquery engines 134, found in interactive expert systems for diagnosingmedical conditions, can interactively query the patient. Using thepersonal computer 18 (shown in FIG. 1), the patient can have aninteractive dialogue with the automated server system 16, as well ashuman experts as necessary, to self assess his or her medical condition.Such expert systems are well known in the art, an example of which isthe MYCIN expert system developed at Stanford University and describedin Buchanan, B. & Shortlife, E., “RULE-BASED EXPERT SYSTEMS. The MYCINExperiments of the Stanford Heuristic Programming Project,”Addison-Wesley (1984). The various forms of feedback described abovehelp to increase the accuracy and specificity of the reporting of thequality of life and symptomatic measures.

FIGS. 8A-8B are flow diagrams showing a method for diagnosing andmonitoring respiratory insufficiency and outcomes thereof 135 using anautomated collection and analysis patient care system 10 in accordancewith the present invention. First, the indicator thresholds 129 (shownin FIG. 5) are set (block 136) by defining a quantifiable physiologicalmeasure of a pathophysiology indicative of respiratory insufficiency andrelating to the each type of patient information in the combined deviceand derived measures set 95 (shown in FIG. 4). The actual values of eachindicator threshold can be finite cutoff values, weighted values, orstatistical ranges, as discussed below with reference to FIGS. 11A-11F.Next, the reference baseline 26 (block 137) and monitoring sets 27(block 138) are retrieved from the database 17, as further describedbelow with reference to FIGS. 9 and 10, respectively. Each measure inthe combined device and derived measures set 95 is tested against thethreshold limits defined for each indicator threshold 129 (block 139),as further described below with reference to FIGS. 11A-11F. Thepotential onset, progression, regression, or status quo of respiratoryinsufficiency is then evaluated (block 140) based upon the findings ofthe threshold limits tests (block 139), as further described below withreference to FIGS. 13A-13C, 14A-14C, 15A-15C.

In a further embodiment, multiple near-simultaneous disorders areconsidered in addition to primary respiratory insufficiency. Primaryrespiratory insufficiency is defined as the onset or progression ofrespiratory insufficiency without obvious inciting cause. Secondaryrespiratory insufficiency is defined as the onset or progression ofrespiratory insufficiency (in a patient with or without pre-existingrespiratory insufficiency) from another disease process, such ascongestive heart failure, coronary insufficiency, atrial fibrillation,and so forth. Other health disorders and diseases can potentially sharethe same forms of symptomatology as respiratory insufficiency, such ascongestive heart failure, myocardial ischemia, pneumonia, exacerbationof chronic bronchitis, renal failure, sleep-apnea, stroke, anemia,atrial fibrillation, other cardiac arrhythmias, and so forth. If morethan one abnormality is present, the relative sequence and magnitude ofonset of abnormalities in the monitored measures becomes most importantin sorting and prioritizing disease diagnosis and treatment.

Thus, if other disorders or diseases are being cross-referenced anddiagnosed (block 141), their status is determined (block 142). In thedescribed embodiment, the operations of ordering and prioritizingmultiple near-simultaneous disorders (box 151) by the testing ofthreshold limits and analysis in a manner similar to congestive heartfailure as described above, preferably in parallel to the presentdetermination, is described in the related, commonly assigned U.S. Pat.No. 6,440,066, entitled “Automated Collection And Analysis Patient CareSystem And Method For Ordering And Prioritizing Multiple HealthDisorders To Identify An Index Disorder,” issued Aug. 27, 2002, thedisclosure of which is incorporated herein by reference. If respiratoryinsufficiency is due to an obvious inciting cause, i.e., secondaryrespiratory insufficiency, (block 143), an appropriate treatment regimenfor respiratory insufficiency as exacerbated by other disorders isadopted that includes treatment of secondary disorders, e.g., congestiveheart failure, myocardial ischemia, atrial fibrillation, and so forth(block 144) and a suitable patient status indicator 127 for respiratoryinsufficiency is provided (block 146) to the patient. Suitable devicesand approaches to diagnosing and treating congestive heart failure,myocardial infarction, and atrial fibrillation are described in related,commonly assigned U.S. Pat. No. 6,336,903, entitled “AutomatedCollection And Analysis Patient Care System And Method For DiagnosingAnd Monitoring Congestive Heart Failure And Outcomes Thereof,” issuedJan. 8, 2002; U.S. Pat. No. 6,368,284, entitled “Automated CollectionAnd Analysis Patient Care System And Method For Diagnosing AndMonitoring Myocardial Ischemia And Outcomes Thereof,” issued Apr. 9,2002; and U.S. Pat. No. 6,411,840, entitled “Automated Collection AndAnalysis Patient Care System And Method For Diagnosing And MonitoringThe Outcomes Of Atrial Fibrillation,” Jun. 15, 2002, the disclosures ofwhich are incorporated herein by reference.

Otherwise, if primary respiratory insufficiency is indicated (block143), a primary treatment regimen is followed (block 145). A patientstatus indicator 127 for respiratory insufficiency is provided (block146) to the patient regarding physical well-being, disease prognosis,including any determinations of disease onset, progression, regression,or status quo, and other pertinent medical and general information ofpotential interest to the patient.

Finally, in a further embodiment, if the patient submits a query to theserver system 16 (block 147), the patient query is interactivelyprocessed by the patient query engine (block 148). Similarly, if theserver elects to query the patient (block 149), the server query isinteractively processed by the server query engine (block 150). Themethod then terminates if no further patient or server queries aresubmitted.

FIG. 9 is a flow diagram showing the routine for retrieving referencebaseline sets 137 for use in the method of FIGS. 8A-8B. The purpose ofthis routine is to retrieve the appropriate reference baseline sets 26,if used, from the database 17 based on the types of comparisons beingperformed. First, if the comparisons are self referencing with respectto the measures stored in the individual patient care record 23 (block152), the reference device and derived measures set 24 a and referencequality of life and symptom measures set 25 a, if used, are retrievedfor the individual patient from the database 17 (block 153). Next, ifthe comparisons are peer group referencing with respect to measuresstored in the patient care records 23 for a health disorder- ordisease-specific peer group (block 154), the reference device andderived measures set 24 a and reference quality of life and symptommeasures set 25 a, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 155). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation (SD), andtrending data) from the reference baseline 26 for the peer group is thencalculated (block 156). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 157), the reference deviceand derived measures set 24 a and reference quality of life and symptommeasures set 25 a, if used, are retrieved from each patient care record23 from the database 17 (block 158). Minimum, maximum, averaged,standard deviation, and trending data and other numerical processesusing the data, as is known in the art, for each measure from thereference baseline 26 for the peer group is then calculated (block 159).The routine then returns.

FIG. 10 is a flow diagram showing the routine for retrieving monitoringsets 138 for use in the method of FIGS. 8A-8B. The purpose of thisroutine is to retrieve the appropriate monitoring sets 27 from thedatabase 17 based on the types of comparisons being performed. First, ifthe comparisons are self referencing with respect to the measures storedin the individual patient care record 23 (block 160), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved for the individual patient from thedatabase 17 (block 161). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 162),the device and derived measures set 24 b and quality of life and symptommeasures set 25 b, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 163). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation, andtrending data) from the monitoring sets 27 for the peer group is thencalculated (block 164). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 165), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved from each patient care record 23 from thedatabase 17 (block 166). Minimum, maximum, averaged, standard deviation,and trending data and other numerical processes using the data, as isknown in the art, for each measure from the monitoring sets 27 for thepeer group is then calculated (block 167). The routine then returns.

FIGS. 11A-11F are flow diagrams showing the routine for testingthreshold limits 139 for use in the method of FIGS. 8A and 8B. Thepurpose of this routine is to analyze, compare, and log any differencesbetween the observed, objective measures stored in the referencebaseline 26, if used, and the monitoring sets 27 to the indicatorthresholds 129. Briefly, the routine consists of tests pertaining toeach of the indicators relevant to diagnosing and monitoring respiratoryinsufficiency. The threshold tests focus primarily on: (1) changes toand rates of change for the indicators themselves, as stored in thecombined device and derived measures set 95 (shown in FIG. 4) or similardata structure; and (2) violations of absolute threshold limits whichtrigger an alert. The timing and degree of change may vary with eachmeasure and with the natural fluctuations noted in that measure duringthe reference baseline period. In addition, the timing and degree ofchange might also vary with the individual and the natural history of ameasure for that patient.

One suitable approach to performing the threshold tests uses a standardstatistical linear regression technique using a least squares error fit.The least squares error fit can be calculated as follows:$\begin{matrix}{y = {\beta_{0} + {\beta_{1}x}}} & (1) \\{\beta = \frac{{SS}_{xy}}{{SS}_{xx}}} & (2) \\{{SS}_{xy} = {{\sum\limits_{i = 1}^{n}\quad{x_{i}y_{i}}} - \frac{\left( {\sum\limits_{i = 1}^{n}\quad x_{i}} \right)\left( {\sum\limits_{i = 1}^{n}\quad y_{i}} \right)}{n}}} & (3) \\{{SS}_{xx} = {{\sum\limits_{i = 1}^{n}\quad x_{i}^{2}} - \frac{\left( {\sum\limits_{i = 1}^{n}\quad x_{i}} \right)^{2}}{n}}} & (4)\end{matrix}$where n is the total number of measures, x_(i) is the time of day formeasure i, and y_(i) is the value of measure i, β₁ is the slope, and β₀is the y-intercept of the least squares error line. A positive slope β₁indicates an increasing trend, a negative slope β₁ indicates adecreasing trend, and no slope indicates no change in patient conditionfor that particular measure. A predicted measure value can be calculatedand compared to the appropriate indicator threshold 129 for determiningwhether the particular measure has either exceeded an acceptablethreshold rate of change or the absolute threshold limit.

For any given patient, three basic types of comparisons betweenindividual measures stored in the monitoring sets 27 are possible: selfreferencing, peer group, and general population, as explained above withreference to FIG. 6. In addition, each of these comparisons can includecomparisons to individual measures stored in the pertinent referencebaselines 24.

The indicator thresholds 129 for detecting a trend indicatingprogression into a state of respiratory insufficiency or a state ofimminent or likely respiratory insufficiency, for example, over a oneweek time period, can be as follows:

-   -   (1) Respiratory rate (block 170): If the respiratory rate has        increased over 1.0 SD from the mean respiratory rate in the        reference baseline 26 (block 171), the increased respiratory        rate and time span over which it occurs are logged in the        combined measures set 95 (block 172).    -   (2) Heart rate (block 173): If the heart rate has increased over        1.0 SD from the mean heart rate in the reference baseline 26        (block 174), the increased heart rate and time span over which        it occurs are logged in the combined measures set 95 (block        175).    -   (3) Transthoracic impedance (block 176): If the transthoracic        impedance has increased over 1.0 SD from the mean transthoracic        impedance in the reference baseline 26 (block 177), the        increased transthoracic impedance and time span are logged in        the combined measures set 95 (block 178).    -   (4) The ventilatory tidal volume (block 179): If the tidal        volume has increased over 1.0 SD from the tidal volume score in        the reference baseline 26 (block 180), the increased tidal        volume score and time span are logged in the combined measures        set 95 (block 181).    -   (5) Arterial oxygen score (block 182): If the arterial oxygen        score has decreased over 1.0 SD from the arterial oxygen score        in the reference baseline 26 (block 183), the decreased arterial        oxygen score and time span are logged in the combined measures        set 95 (block 184).        (6) Arterial carbon dioxide score (block 185): If the arterial        carbon dioxide score has decreased over 1.0 SD from the arterial        carbon dioxide score in the reference baseline 26 (block 186),        the decreased arterial carbon dioxide score and time span are        logged in the combined measures set 95 (block 187).    -   (7) Patient activity score (block 188): If the mean patient        activity score has decreased over 1.0 SD from the mean patient        activity score in the reference baseline 26 (block 189), the        decreased patient activity score and time span are logged in the        combined measures set 95 (block 190).    -   (8) Temperature (block 191): If the patient temperature score        has increased over 1.0 SD from the mean patient temperature        score in the reference baseline 26 (block 192), the increased        patient temperature score and the time span are logged in the        combined measures set 95 (block 193).    -   (9) Spikes in patient activity (block 194): If short-lived        spikes in the patient activity score occur over time periods        under 5 minutes compared to the reference baseline 26 (block        195), the spike in patient activity score and time span are        logged in the combined measures set 95 (block 196).    -   (10) Spikes in pulmonary arterial pressure (PAP) (block 197): If        short-lived spikes in the pulmonary arterial pressure score        occur over time periods under 5 minutes compared to the        reference baseline 26 (block 198), the spike in the pulmonary        arterial pressure score and time span are logged in the combined        measures set 95 (block 199). In the described embodiment, the        mean arterial pressure on any spike in the arterial pressure        tracing could be utilized.    -   (11) Exercise tolerance quality of life (QOL) measures (block        200): If the exercise tolerance QOL has decreased over 1.0 SD        from the mean exercise tolerance in the reference baseline 26        (block 201), the decrease in exercise tolerance and the time        span over which it occurs are logged in the combined measures        set 95 (block 202).    -   (12) Respiratory distress quality of life (QOL) measures (block        203): If the respiratory distress QOL measure has deteriorated        by more than 1.0 SD from the mean respiratory distress QOL        measure in the reference baseline 26 (block 204), the increase        in respiratory distress and the time span over which it occurs        are logged in the combined measures set 95 (block 205).    -   (13) Spikes in right ventricular (RV) pressure (block 206): If        short-lived spikes in the right ventricular pressure occur over        time periods under 5 minutes compared to the reference baseline        26 (block 207), the spike in the right ventricular pressure and        time span are logged in the combined measures set 95 (block        208).    -   (14) Spikes in transthoracic impedance (TTZ) (block 209): If        short-lived spikes in the transthoracic impedance occur over        time periods under 5 minutes compared to the reference baseline        26 (block 210), the spike in the transthoracic impedance and        time span are logged in the combined measures set 95 (block        211).    -   (15) Atrial fibrillation (block 212): The presence or absence of        atrial fibrillation (AF) is determined and, if present (block        213), atrial fibrillation is logged (block 214).    -   (16) Rhythm changes (block 215): The type and sequence of rhythm        changes is significant and is determined (block 216) based on        the timing of the relevant rhythm measure, such as sinus rhythm.        For instance, a finding that a rhythm change to atrial        fibrillation precipitated respiratory measures changes can        indicate therapy directions against atrial fibrillation rather        than the primary development of respiratory insufficiency. Thus,        if there are rhythm changes (block 217), the sequence of the        rhythm changes and time span are logged (block 211).

Note also that an inversion of the indicator thresholds 129 definedabove could similarly be used for detecting a trend in diseaseregression. One skilled in the art would recognize that these measureswould vary based on whether or not they were recorded during rest orduring activity and that the measured activity score can be used toindicate the degree of patient rest or activity. The patient activityscore can be determined via an implantable motion detector, for example,as described in U.S. Pat. No. 4,428,378, issued Jan. 31, 1984, toAnderson et al., the disclosure of which is incorporated herein byreference.

The indicator thresholds 129 for detecting a trend towards a state ofrespiratory insufficiency can also be used to declare, a priori,respiratory insufficiency present, regardless of pre-existing trend datawhen certain limits are established, such as:

-   -   (1) An absolute limit of arterial oxygen (block 182) less than        85 mm Hg is an a priori definition of respiratory insufficiency        from decreased oxygenation.    -   (2) An absolute limit of arterial carbon dioxide (block 185)        falling below 25 mm Hg (in the absence of marked exercise) or        greater than 50 mm Hg are both a priori definitions of        respiratory insufficiency as indicated by hyperventilation and        hypoventilation, respectively.

FIG. 12 is a flow diagram showing the routine for evaluating the onset,progression, regression and status quo of respiratory insufficiency 140for use in the method of FIGS. 8A and 8B. The purpose of this routine isto evaluate the presence of sufficient indicia to warrant a diagnosis ofthe onset, progression, regression, and status quo of respiratoryinsufficiency. Quality of life and symptom measures 25 a, 25 b can beincluded in the evaluation (block 230) by determining whether any of theindividual quality of life and symptom measures 25 a, 25 b have changedrelative to the previously collected quality of life and symptommeasures from the monitoring sets 27 and the reference baseline 26, ifused. For example, an increase in the shortness of breath measure 87 andexercise tolerance measure 92 would corroborate a finding of respiratoryinsufficiency. Similarly, a transition from NYHA Class II to NYHA ClassIII would indicate a deterioration or, conversely, a transition fromNYHA Class III to NYHA Class II status would indicate improvement orprogress when adapting the NYHA classifications for their parallel inlung disorders. Incorporating the quality of life and symptom measures25 a, 25 b into the evaluation can help, in part, to refute or supportfindings based on physiological data. Next, a determination as towhether any changes to interventive measures are appropriate based onthreshold stickiness (“hysteresis”) is made (block 231), as furtherdescribed below with reference to FIG. 16.

The routine returns upon either the determination of a finding orelimination of all factors as follows. If a finding of respiratoryinsufficiency was not previously diagnosed (block 232), a determinationof disease onset is made (block 233), as further described below withreference to FIGS. 13A-13C. Otherwise, if respiratory insufficiency waspreviously diagnosed (block 232), a further determination of eitherdisease progression or worsening (block 234) or regression or improving(block 235) is made, as further described below with reference to FIGS.14A-14C and 15A-15C, respectively. If, upon evaluation, neither diseaseonset (block 233), worsening (block 234) or improving (block 235) isindicated, a finding of status quo is appropriate (block 236) and noted(block 237). Otherwise, respiratory insufficiency and the relatedoutcomes are actively managed (block 238) through the administration of,non-exclusively, antibiotic and antiviral therapies, bronchodilatortherapies, oxygen therapies, antiinflammation therapies, electricaltherapies, mechanical therapies, and other therapies as are known in theart. The management of respiratory insufficiency is described, by way ofexample, in A. S. Fauci et al. (Eds.), “Harrison's Principles ofInternal Medicine,” pp. 1407-1491, McGraw-Hill, 14^(th) Ed. (1997), thedisclosure of which is incorporated herein by reference. The routinethen returns.

FIGS. 13A-13C are flow diagrams showing the routine for determining anonset of respiratory insufficiency 232 for use in the routine of FIG.12. Respiratory insufficiency is possible based on two general symptomcategories: reduced exercise capacity (block 244) and respiratorydistress (block 256). An effort is made to diagnose respiratoryinsufficiency manifesting primarily as resulting in reduced exercisecapacity (block 244) and/or increased respiratory distress (block 256).Reduced exercise capacity and respiratory distress can generally serveas markers of low systemic arterial oxygenation. The clinical aspects ofrespiratory insufficiency are described, by way of example, in A. S.Fauci et al. (Eds.), “Harrison's Principles of Internal Medicine,” pp.1410-1419, McGraw-Hill, 14^(th) Ed. (1997), the disclosure of which isincorporated herein by reference.

As primary pulmonary disease considerations, multiple individualindications (blocks 240-243, 245-253) should be present for the twoprincipal findings of respiratory insufficiency related reduced exercisecapacity (block 244), or respiratory insufficiency related respiratorydistress (block 256), to be indicated, both for disease onset orprogression. The presence of primary key findings alone can besufficient to indicate an onset of respiratory insufficiency andsecondary key findings serve to corroborate disease onset. Note thepresence of any abnormality can trigger an analysis for the presence orabsence of secondary disease processes, such as the presence of atrialfibrillation or congestive heart failure. Secondary diseaseconsiderations can be evaluated using the same indications (see, e.g.,blocks 141-144 of FIGS. 8A-8B), but with adjusted indicator thresholds129 (shown in FIG. 5) triggered at a change of 0.5 SD, for example,instead of 1.0 SD.

In the described embodiment, the reduced exercise capacity andrespiratory distress findings (blocks 244, 251) can be established byconsolidating the individual indications (blocks 240-243, 245-253) inseveral ways. First, in a preferred embodiment, each individualindication (blocks 240-243, 245-253) is assigned a scaled index valuecorrelating with the relative severity of the indication. For example,decreased cardiac output (block 240) could be measured on a scale from‘1’ to ‘5’ wherein a score of ‘1’ indicates no change in cardiac outputfrom the reference point, a score of ‘2’ indicates a change exceeding0.5 SD, a score of ‘3’ indicates a change exceeding 1.0 SD, a score of‘4’ indicates a change exceeding 2.0 SD, and a score of ‘5’ indicates achange exceeding 3.0 SD. The index value for each of the individualindications (blocks 240-243, 245-253) can then either be aggregated oraveraged with a result exceeding the aggregate or average maximumindicating an appropriate respiratory insufficiency finding.

Preferably, all scores are weighted depending upon the assignments madefrom the measures in the reference baseline 26. For instance, arterialpartial pressure of oxygen 102 could be weighted more importantly thanrespiratory rate 104 if the respiratory rate in the reference baseline26 is particularly high at the outset, making the detection of furtherdisease progression from increases in respiratory rate, less sensitive.In the described embodiment, arterial partial pressure of oxygen 102receives the most weight in determining a reduced exercise capacityfinding whereas arterial partial pressure of carbon dioxide 107 receivesthe most weight in determining a respiratory distress or dyspneafinding.

Alternatively, a simple binary decision tree can be utilized whereineach of the individual indications (blocks 240-243, 245-253) is eitherpresent or is not present. All or a majority of the individualindications (blocks 240-243, 245-253) should be present for the relevantrespiratory insufficiency finding to be affirmed.

Other forms of consolidating the individual indications (blocks 240-243,245-253) are feasible.

FIGS. 14A-14C are flow diagrams showing the routine for determining aprogression or worsening of respiratory insufficiency 234 for use in theroutine of FIG. 12. The primary difference between the determinations ofdisease onset, as described with reference to FIGS. 13A-13C, and diseaseprogression is the evaluation of changes indicated in the same factorspresent in a disease onset finding. Thus, a revised respiratoryinsufficiency finding is possible based on the same two general symptomcategories: reduced exercise capacity (block 274) and respiratorydistress (block 286). The same factors which need be indicated towarrant a diagnosis of respiratory insufficiency onset are evaluated todetermine disease progression.

Similarly, FIGS. 15A-15C are flow diagrams showing the routine fordetermining a regression or improving of respiratory distress 235 foruse in the routine of FIG. 12. The same factors as described above withreference to FIGS. 13A-13C and 14A-14C, trending in opposite directionsfrom disease onset or progression, are evaluated to determine diseaseregression. As primary cardiac disease considerations, multipleindividual indications (blocks 300-303, 305-313) should be present forthe two principal findings of respiratory insufficiency related reducedexercise capacity (block 304), or respiratory insufficiency relatedrespiratory distress (block 316), to indicate disease regression.

FIG. 16 is a flow diagram showing the routine for determining thresholdstickiness (“hysteresis”) 231 for use in the method of FIG. 12.Stickiness, also known as hysteresis, is a medical practice doctrinewhereby a diagnosis or therapy will not be changed based upon small ortemporary changes in a patient reading, even though those changes mighttemporarily move into a new zone of concern. For example, if a patientmeasure can vary along a scale of ‘1’ to ‘10’ with ‘10’ being worse, atransient reading of ‘6,’ standing alone, on a patient who hasconsistently indicated a reading of ‘5’ for weeks will not warrant achange in diagnosis without a definitive prolonged deterioration firstbeing indicated. Stickiness dictates that small or temporary changesrequire more diagnostic certainty, as confirmed by the persistence ofthe changes, than large changes would require for any of the monitored(device) measures. Stickiness also makes reversal of importantdiagnostic decisions, particularly those regarding life-threateningdisorders, more difficult than reversal of diagnoses of modest import.As an example, automatic external defibrillators (AEDs) manufactured byHeartstream, a subsidiary of Agilent Technologies, Seattle, Wash.,monitor heart rhythms and provide interventive shock treatment for thediagnosis of ventricular fibrillation. Once diagnosis of ventricularfibrillation and a decision to shock the patient has been made, apattern of no ventricular fibrillation must be indicated for arelatively prolonged period before the AED changes to a “no-shock”decision. As implemented in this AED example, stickiness mandatescertainty before a decision to shock is disregarded. In practice,stickiness also dictates that acute deteriorations in disease state aretreated aggressively while chronic, more slowly progressing diseasestates are treated in a more tempered fashion. However, in the case ofsome of the parameters being followed, such as activity and pulmonaryartery systolic pressure, abrupt spikes in these measures can beindicative of coughing and therefore helpful in indicating the onset ofa disorder that might lead to pulmonary insufficiency.

Thus, if the patient status indicates a status quo (block 330), nochanges in treatment or diagnosis are indicated and the routine returns.Otherwise, if the patient status indicates a change away from status quo(block 330), the relative quantum of change and the length of time overwhich the change has occurred is determinative. If the change ofapproximately 0.5 SD has occurred over the course of about one month(block 331), a gradually deteriorating condition exists (block 332) anda very tempered diagnostic, and if appropriate, treatment program isundertaken. If the change of approximately 1.0 SD has occurred over thecourse of about one week (block 333), a more rapidly deterioratingcondition exists (block 334) and a slightly more aggressive diagnostic,and if appropriate, treatment program is undertaken. If the change ofapproximately 2.0 SD has occurred over the course of about one day(block 335), an urgently deteriorating condition exists (block 336) anda moderately aggressive diagnostic, and if appropriate, treatmentprogram is undertaken. If the change of approximately 3.0 SD hasoccurred over the course of about one hour (block 337), an emergencycondition exists (block 338) and an immediate diagnostic, and ifappropriate, treatment program is undertaken as is practical. Finally,if the change and duration fall outside the aforementioned ranges(blocks 331-338), an exceptional condition exists (block 339) and thechanges are reviewed manually, if necessary. The routine then returns.These threshold limits and time ranges may then be adapted dependingupon patient history and peer-group guidelines.

The present invention provides several benefits. One benefit is improvedpredictive accuracy from the outset of patient care when a referencebaseline is incorporated into the automated diagnosis. Another benefitis an expanded knowledge base created by expanding the methodologiesapplied to a single patient to include patient peer groups and theoverall patient population. Collaterally, the information maintained inthe database could also be utilized for the development of furtherpredictive techniques and for medical research purposes. Yet a furtherbenefit is the ability to hone and improve the predictive techniquesemployed through a continual reassessment of patient therapy outcomesand morbidity rates.

Other benefits include an automated, expert system approach to thecross-referral, consideration, and potential finding or elimination ofother diseases and health disorders with similar or related etiologicalindicators and for those other disorders that may have an impact onrespiratory insufficiency. Although disease specific markers will provevery useful in discriminating the underlying cause of symptoms, manydiseases, other than respiratory insufficiency, will alter some of thesame physiological measures indicative of respiratory insufficiency.Consequently, an important aspect of considering the potential impact ofother disorders will be, not only the monitoring of disease specificmarkers, but the sequencing of change and the temporal evolution of moregeneral physiological measures, for example respiratory rate, pulmonaryartery diastolic pressure, and cardiac output, to reflect disease onset,progression or regression in more than one type of disease process,especially congestive heart failure from whatever cause.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

1. A system for managing respiratory insufficiency in conjunction withheart failure assessment, comprising: a data module to assemblephysiological measures, which were directly recorded as data on asubstantially continuous basis by a medical device for a patient orindirectly derived from the data; a status module to determine a statusfor the patient through sampling and analysis of the physiologicalmeasures over a plurality of data assembly points; and an evaluationmodule to evaluate the physiological measures relative to the patientstatus, comprising: an analysis module to analyze any trend comprisingone of a status quo and a change in at least one of cardiac performanceand respiratory performance; and a comparison module to compare thetrend to applicable indications of worsening heart failure andrespiratory insufficiency.
 2. A system according to claim 1, wherein thetrend relates to at least one of a finding of reduced exercise capacityand respiratory distress.
 3. A system according to claim 2, wherein thefinding of reduced exercise capacity is selected from the groupcomprising decreased cardiac output, decreased mixed venous oxygenscore, decreased patient activity score and decreased exercisetolerance.
 4. A system according to claim 2, wherein the finding ofrespiratory distress is selected from the group comprising a spike inpatient activity score, a spike in pulmonary artery pressure, a spike inright ventricular pressure, a spike in transthoracic impedance,increased respiratory rate, increased minute ventilation, increasedtemperature, decreased QT interval, decreased arterial oxygen anddecreased arterial carbon dioxide.
 5. A system according to claim 1,further comprising: a quality of life module to associate quality oflife measures chronicled by the patient with the physiological measures.6. A system according to claim 1, further comprising: an indicatormodule to define stickiness indicators comprising a temporal bound onchange of one or more of the physiological measures; an evaluationmodule to evaluate a time span occurring between a plurality of the dataassembly points, which are distinct; and a revision module to revisepatient treatment in a manner corresponding to a rate of the change overthe time span.
 7. A system according to claim 1, wherein the change inat least one of cardiac performance and respiratory performance isselected from the group comprising an onset, progression, and regressionof either a cardiac performance or a respiratory performance parameter.8. A system according to claim 1, wherein the worsening heart failureindications are selected from the group comprising pulmonary arterypressure, left atrial pressure, dyspnea, orthopnea, pulmonary edema,peripheral edema, and fatigue.
 9. A system according to claim 1, furthercomprising: a measurement module to measure one or more of pulmonaryartery pressure, heart rate, heart sounds, intrathoracic impedance,respiration, posture, lung fluid, activity, weight, and physiologicalresponse to activity.
 10. A system according to claim 1, furthercomprising: a reprogramming module to reprogram a medical device basedon evaluation of the physiological measures.
 11. A system according toclaim 10, wherein either the worsening heart failure indications or therespiratory insufficiency are factored into the reprogramming.
 12. Asystem according to claim 1, wherein respiration rate is tracked throughthe medical device, further comprising: a notification module togenerate a notification triggered by a parameter assigned to therespiration rate.
 13. A system according to claim 12, wherein theparameter comprises one or more of an upper limit parameter applied overa short term and a counter parameter applied over a long term.
 14. Asystem according to claim 1, wherein the medical device comprises one ofan implantable medical device and an external medical device.
 15. Amethod for managing respiratory insufficiency in conjunction with heartfailure assessment, comprising: assembling physiological measures, whichwere directly recorded as data on a substantially continuous basis by amedical device for a patient or indirectly derived from the data;determining a status for the patient through sampling and analysis ofthe physiological measures over a plurality of data assembly points; andevaluating the physiological measures relative to the patient status byanalyzing any trend comprising one of a status quo and a change in atleast one of cardiac performance and respiratory performance andcomparing the trend to applicable indications of worsening heart failureand respiratory insufficiency.
 16. A method according to claim 15,wherein the trend relates to at least one of a finding of reducedexercise capacity and respiratory distress.
 17. A method according toclaim 16, wherein the finding of reduced exercise capacity is selectedfrom the group comprising decreased cardiac output, decreased mixedvenous oxygen score, decreased patient activity score and decreasedexercise tolerance.
 18. A method according to claim 16, wherein thefinding of respiratory distress is selected from the group comprising aspike in patient activity score, a spike in pulmonary artery pressure, aspike in right ventricular pressure, a spike in transthoracic impedance,increased respiratory rate, increased minute ventilation, increasedtemperature, decreased QT interval, decreased arterial oxygen anddecreased arterial carbon dioxide.
 19. A method according to claim 15,further comprising: associating quality of life measures chronicled bythe patient with the physiological measures.
 20. A method according toclaim 15, further comprising: defining stickiness indicators comprisinga temporal bound on change of one or more of the physiological measures;evaluating a time span occurring between a plurality of the dataassembly points, which are distinct; and revising patient treatment in amanner corresponding to a rate of the change over the time span.
 21. Amethod according to claim 15, wherein the change in at least one ofcardiac performance and respiratory performance is selected from thegroup comprising an onset, progression, and regression of either acardiac performance or a respiratory performance parameter.
 22. A methodaccording to claim 15, wherein the worsening heart failure indicationsare selected from the group comprising pulmonary artery pressure, leftatrial pressure, dyspnea, orthopnea, pulmonary edema, peripheral edema,and fatigue.
 23. A method according to claim 15, further comprising:measuring one or more of pulmonary artery pressure, heart rate, heartsounds, intrathoracic impedance, respiration, posture, lung fluid,activity, weight, and physiological response to activity.
 24. A methodaccording to claim 15, further comprising: reprogramming a medicaldevice based on evaluation of the physiological measures.
 25. A methodaccording to claim 24, further comprising: factoring either theworsening heart failure indications or the respiratory insufficiencyinto the reprogramming.
 26. A method according to claim 15, furthercomprising: tracking respiration rate through the medical device; andgenerating a notification triggered by a parameter assigned to therespiration rate.
 27. A method according to claim 26, wherein theparameter comprises one or more of an upper limit parameter applied overa short term and a counter parameter applied over a long term.
 28. Amethod according to claim 15, wherein the medical device comprises oneof an implantable medical device and an external medical device.
 29. Asystem for evaluating a patient status from sampled physiometry for usein respiratory insufficiency management and heart failure assessment,comprising: a storage module to store physiological measures comprisingat least one of direct measures regularly recorded on a substantiallycontinuous basis by a medical device for a patient and measures derivedfrom the direct measures; a sampling module to sample at least one ofthose of the physiological measures, which each relate to a same type ofphysiometry, and those of the physiological measures, which each relateto a different type of physiometry; a status module to determine astatus for the patient through analysis of those sampled physiologicalmeasures assembled from a plurality of recordation points; and anevaluation module to evaluate the sampled physiological measures,comprising: an analysis module to identify any trends that are indicatedby the patient status comprising one of a status quo and a change, whichmight affect one or more of cardiac performance and respiratoryperformance of the patient; and a comparison module to compare each suchtrend to applicable indications of worsening heart failure andrespiratory insufficiency to generate a notification of parameterviolations.
 30. A system according to claim 29, further comprising: areprogramming module to reprogram a medical device based on extendedevaluation of the direct measures and the derived measures.
 31. A systemaccording to claim 29, further comprising: a tracking module to trackrespiration rate of the patient on a regular basis through the medicaldevice; and a notification module to generate a notification triggeredby one or more of an upper limit parameter applied over a short term anda counter parameter applied over a long term.
 32. A method forevaluating a patient status from sampled physiometry for use inrespiratory insufficiency management and heart failure assessment,comprising: storing physiological measures comprising at least one ofdirect measures regularly recorded on a substantially continuous basisby a medical device for a patient and measures derived from the directmeasures; sampling at least one of those of the physiological measures,which each relate to a same type of physiometry, and those of thephysiological measures, which each relate to a different type ofphysiometry; determining a status for the patient through analysis ofthose sampled physiological measures assembled from a plurality ofrecordation points; and evaluating the sampled physiological measures,comprising: identifying any trends that are indicated by the patientstatus comprising one of a status quo and a change, which might affectone or more of cardiac performance and respiratory performance of thepatient; and comparing each such trend to applicable indications ofworsening heart failure and respiratory insufficiency to generate anotification of parameter violations.
 33. A method according to claim32, further comprising: reprogramming a medical device based on extendedevaluation of the direct measures and the derived measures.
 34. A methodaccording to claim 32, further comprising: tracking respiration rate ofthe patient on a regular basis through the medical device; andgenerating a notification triggered by one or more of an upper limitparameter applied over a short term and a counter parameter applied overa long term.